Computer Intensive Methods in Statistics

Regular price €186.00
A01=Behrang Mahjani
A01=Silvelyn Zwanzig
ABC Algorithm
ABC Method
Age Group_Uncategorized
Age Group_Uncategorized
Author_Behrang Mahjani
Author_Silvelyn Zwanzig
automatic-update
Basic Bootstrap Confidence Interval
bootstrap
Bootstrap Confidence Interval
Bootstrap Hypothesis Test
Bootstrap Replications
Bootstrap Sample
Bootstrap World
Bootstrapped Time Series
Category1=Non-Fiction
Category=PBT
Category=UFM
Conditional Expectation
Congruential Generator
COP=United Kingdom
Data Set
Delivery_Pre-order
Distribution Function
EIV Model
EM algorithms
eq_computing
eq_isMigrated=2
eq_non-fiction
Inverse Distribution Function
ISE
Kernel Density Estimator
Language_English
MC
Metropolis Hastings Ratio
monte carlo methods
Orthogonal Series Estimator
PA=Temporarily unavailable
Percentile Bootstrap Interval
Price_€100 and above
PS=Active
Random Walk Metropolis
Ridge Estimator
Smooth Spline
softlaunch
statistical computing
Wavelet Estimators

Product details

  • ISBN 9780367194253
  • Weight: 420g
  • Dimensions: 156 x 234mm
  • Publication Date: 02 Dec 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
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This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners.

Features

  • Presents the main ideas of computer-intensive statistical methods
  • Gives the algorithms for all the methods
  • Uses various plots and illustrations for explaining the main ideas
  • Features the theoretical backgrounds of the main methods.
  • Includes R codes for the methods and examples

Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics.

Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.

Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics.

Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.